The AI boom defined
The AI boom is a period of rapid growth in the field of artificial intelligence beginning in the mid-2010s and accelerating in the 2020s. Generative AI technologies include large language models (LLM), which generate text and code, image models, and video models, and world models. This period is sometimes referred to as an AI spring, a term used to contrast it with previous AI winters.
Key advances preceding the boom include the transformer (2017), generative pre-trained transformer (2018) and reinforcement learning from human feedback. During the boom, LLMs have evolved via the reasoning model, mixture of experts technique, and multimodal learning. As of 2026, leading proprietary LLMs have been released by Anthropic, OpenAI, and divisions of Google, Meta, and SpaceX in the United States. In China, companies favour open weights releases, led by Alibaba Cloud, DeepSeek, Moonshot AI, and Z.ai.
Controversies of the AI boom include its environmental impact, dominance by US Big Tech companies, effect on copyright, proliferation of deepfakes, threats to cybersecurity and biosecurity, its use in warfare, a potential economic bubble, and existential risks such as AI takeover resulting from artificial general intelligence and superintelligence. Since 2018, observers including Ian Bremmer, Nicholas Thompson, and Kai-Fu Lee have argued an AI arms race or AI Cold War exists between the US and China.
The boom has impacted the semiconductor industry, causing a global memory supply shortage; As of second quarter 2026, Nvidia is the world's largest corporation by capitalization while TSMC, Broadcom, and Samsung are among the ten largest. ChatGPT is the fifth-most-visited website globally.
In 2020, DeepMind's AlphaFold program, which is designed to predict protein folding, scored more than 90 in CASP's Global distance test (GDT). The structural biologist and Nobel Prize winner Venki Ramakrishnan called the result "a stunning advance on the protein folding problem". The ability to predict protein structures accurately based on the constituent amino acid sequence may accelerate drug discovery and enable a better understanding of diseases.
As time passed, the power of generative AI grew stronger. In 2015, initial popularity began to grow with the release of Google's DeepDream. DeepDream is a generative AI that takes inputs from a previous image and morphs them to produce hallucinogenic images.
In January 2021, OpenAI released DALL-E, allowing for image generation through text prompts. This allows users to generate any image with a simple prompt. Soon after, other powerful models followed DALL-E, such as Google's Gemini.
The popularity of text-to-video generative AI tools grew exponentially. With the release of models such as OpenAI's Sora in 2024, the use of text-to-video tools became normalized, as people used them for advertisements, which saves on production costs and increases production speed.
Generative AI is growing at a rapid rate, outpacing modern-day detection tools. With the common public having access to these tools, it raises concerns about the ethical use of generative AI. There have been multiple occasions where misinformation has been spread over the internet about politics due to a generated or deep-faked video, posing as a security threat.
As of August 2026, large AI models have been released by OpenAI, Anthropic, Google DeepMind, Meta Superintelligence Labs, and SpaceXAI in the United States, and in China by Alibaba Cloud, DeepSeek, Moonshot AI, Z.ai, and ByteDance. Mistral AI, based in France, is the largest European AI company. Open-source AI includes small language models, which are more suited to being run on personal computers. These include many models under Alibaba's Qwen, Google's Gemma, Nvidia's Nemotron, and Meta's Muse Glimmer.
GPT-3 is a large language model that was released in 2020 by OpenAI and is capable of generating human-like text. A new version called GPT-4 was released on 14 March 2023, and was used in the Microsoft Bing search engine.
Generative coding can be used to produce, edit, explain, and debug code. A 2026 study in the journal Management Science found that less experienced developers have higher adoption rates and greater productivity gains.
In 2016, Google's DeepMind produced WaveNet. WaveNet allowed the generation of raw audio of speech and piano. WaveNet is able to generate different voices by identifying the speakers. This acted as a fundamental building block for future models, allowing audio to be formed from scratch. This wouldn't only help with the production of music, but voice generation as well.
OpenAI released Jukebox, the first large-scale model to generate songs, in 2020. Jukebox allowed for raw audio in different genres and styles.
In 2024, AI models capable of producing high-fidelity music became available to the public. In June 2024, AI-generated music services Udio and Suno AI were sued by a group of major record labels over copyright infringement concerns.
In March 2020, 15.ai was founded. 15.ai allowed for audio deepfakes. Artificially generated vocals can be generated with tools such as ElevenLabs, which allows for the creation of vocals from any audio. This allows for any celebrity or politician who has voice clips on the internet to be subject to audio deepfakes for both speech and singing. The voices of politicians, such as Joe Biden, have been used for fake robocalls to voters to attempt to manipulate elections.
The use of AI leads to a request and the needs of more energy. To meet this enormous demand, engineering realities show that the U.S. gas production will increase by 10 to 20% by 2030, as most of the additional electricity generation will mostly come from burning natural gas. Then, after 2030, those demands will probably be met by additional solar capacity and nuclear energy.
Electricity consumed by hardware used for AI has increased demands on power grids, which has led to prolonged use of fossil fuel power plants which would otherwise have been deactivated, since renewable energy sources often lack the continuous baseload power required for 24/7 AI model training and inference.
Microsoft, Google, and Amazon have all invested in existing or proposed nuclear power plants to meet these demands. In September 2024, Microsoft signed a deal with Constellation Energy to purchase power from a reactor at Three Mile Island which had been shut down in 2019. The reactor is set to reopen in 2028 to provide power to Microsoft's data centers. The reactor is next to the unit which caused the worst nuclear power accident in US history in 1979.
According to a report from Pew Research, opinions on AI are divided, with most Americans expressing concerns over a lack of control over AI and potential negative effect on human creativity. Nikolova & Angrisani (2025) found that people are specifically distrustful of the use AI in personal relationships, while being more accepting of its use in medicine, such as for creating new antibiotics.
Some economists have been optimistic about the potential of the current wave of AI to boost productivity and economic growth. Notably, Stanford University economist Erik Brynjolfsson, in a series of articles has argued for an "AI-powered Productivity Boom" and a "Coming Productivity Boom". At the same time, others like Northwestern University economist Robert Gordon remain more pessimistic. Brynjolfsson and Gordon have made a formal bet, registered at long bets, about the rate of productivity growth in the 2020s, to be resolved at the end of the decade.
Big Tech companies view the AI boom as both opportunity and threat; Alphabet's Google, for example, realized that ChatGPT could be an innovator's dilemma-like replacement for Google Search. The company merged DeepMind and Google Brain, a rival internal unit, to accelerate its AI research.
The market capitalization of Nvidia, whose GPUs are in high demand to train and use generative AI models, rose to over US$3.3 trillion, making it the world's largest company by market capitalization as of 19 June 2024 and became the first company to reach US$4 trillion on 9 July 2025 and subsequently US$5 trillion on 29 October later that same year.
In 2023, San Francisco's population increased for the first time in years, with the boom cited as a contributing factor.
Machine learning resources, hardware or software can be bought and licensed off-the-shelf or as cloud platform services.[99] This enables wide and publicly available uses, spreading AI skills. Over half of businesses consider AI to be a top organizational priority and to be the most crucial technological advancement in many decades.
Across industries, generative AI tools are becoming widely available through the AI boom and are increasingly used in businesses across regions. A main area of use is data analytics. Seen as an incremental change, machine learning improves industry performance. Businesses report AI to be most useful in increased process efficiency, improved decision-making and strengthening of existing services and products. Through adoption, AI has already positively influenced revenue generation in multiple business functions. Businesses have experienced revenue increases of up to 16%, mainly in manufacturing, risk management and research and development.
AI and generative AI investments have been increasing with the boom, increasing from $18 billion in 2014 to $119 billion in 2021. Most notably, the share of generative AI investments was around 30% in 2023. Further, generative AI businesses have seen considerable venture capital investments even though regulatory and economic outlooks remain in question.
Tech giants capture the bulk of the monetary gains from AI and act as major suppliers to or customers of private users and other businesses.
With the introduction of AI, there has been an exponential rise in production for businesses. It's expected that workers could use resources provided by artificial intelligence to boost their productivity. As many small businesses don't use AI, it's believed that if it's adopted by more businesses, the whole work structure could be changed, as many tasks will be automated by AI.
The U.S. Census Bureau's Business Trends and Outlook Survey measured AI use at 3–9% of businesses in March 2025, using language that asked whether firms used AI "to produce goods and services". After revising the question to cover "any business function", the Bureau's measured adoption rate increased to 18% in March 2026.
According to Bell & Korinek (2023), the economic effect of AI could worsen economic inequality, which could potentially threaten democracy in ways which are separate from threats caused by AI's use in misinformation and propaganda.
As of second quarter 2026, Nvidia is the world's largest corporation by capitalization, and semiconductor manufacturers TSMC, Broadcom, and Samsung are among the ten largest.
Key advances preceding the boom include the transformer (2017), generative pre-trained transformer (2018) and reinforcement learning from human feedback. During the boom, LLMs have evolved via the reasoning model, mixture of experts technique, and multimodal learning. As of 2026, leading proprietary LLMs have been released by Anthropic, OpenAI, and divisions of Google, Meta, and SpaceX in the United States. In China, companies favour open weights releases, led by Alibaba Cloud, DeepSeek, Moonshot AI, and Z.ai.
Controversies of the AI boom include its environmental impact, dominance by US Big Tech companies, effect on copyright, proliferation of deepfakes, threats to cybersecurity and biosecurity, its use in warfare, a potential economic bubble, and existential risks such as AI takeover resulting from artificial general intelligence and superintelligence. Since 2018, observers including Ian Bremmer, Nicholas Thompson, and Kai-Fu Lee have argued an AI arms race or AI Cold War exists between the US and China.
The boom has impacted the semiconductor industry, causing a global memory supply shortage; As of second quarter 2026, Nvidia is the world's largest corporation by capitalization while TSMC, Broadcom, and Samsung are among the ten largest. ChatGPT is the fifth-most-visited website globally.
In 2020, DeepMind's AlphaFold program, which is designed to predict protein folding, scored more than 90 in CASP's Global distance test (GDT). The structural biologist and Nobel Prize winner Venki Ramakrishnan called the result "a stunning advance on the protein folding problem". The ability to predict protein structures accurately based on the constituent amino acid sequence may accelerate drug discovery and enable a better understanding of diseases.
As time passed, the power of generative AI grew stronger. In 2015, initial popularity began to grow with the release of Google's DeepDream. DeepDream is a generative AI that takes inputs from a previous image and morphs them to produce hallucinogenic images.
In January 2021, OpenAI released DALL-E, allowing for image generation through text prompts. This allows users to generate any image with a simple prompt. Soon after, other powerful models followed DALL-E, such as Google's Gemini.
The popularity of text-to-video generative AI tools grew exponentially. With the release of models such as OpenAI's Sora in 2024, the use of text-to-video tools became normalized, as people used them for advertisements, which saves on production costs and increases production speed.
Generative AI is growing at a rapid rate, outpacing modern-day detection tools. With the common public having access to these tools, it raises concerns about the ethical use of generative AI. There have been multiple occasions where misinformation has been spread over the internet about politics due to a generated or deep-faked video, posing as a security threat.
As of August 2026, large AI models have been released by OpenAI, Anthropic, Google DeepMind, Meta Superintelligence Labs, and SpaceXAI in the United States, and in China by Alibaba Cloud, DeepSeek, Moonshot AI, Z.ai, and ByteDance. Mistral AI, based in France, is the largest European AI company. Open-source AI includes small language models, which are more suited to being run on personal computers. These include many models under Alibaba's Qwen, Google's Gemma, Nvidia's Nemotron, and Meta's Muse Glimmer.
GPT-3 is a large language model that was released in 2020 by OpenAI and is capable of generating human-like text. A new version called GPT-4 was released on 14 March 2023, and was used in the Microsoft Bing search engine.
Generative coding can be used to produce, edit, explain, and debug code. A 2026 study in the journal Management Science found that less experienced developers have higher adoption rates and greater productivity gains.
In 2016, Google's DeepMind produced WaveNet. WaveNet allowed the generation of raw audio of speech and piano. WaveNet is able to generate different voices by identifying the speakers. This acted as a fundamental building block for future models, allowing audio to be formed from scratch. This wouldn't only help with the production of music, but voice generation as well.
OpenAI released Jukebox, the first large-scale model to generate songs, in 2020. Jukebox allowed for raw audio in different genres and styles.
In 2024, AI models capable of producing high-fidelity music became available to the public. In June 2024, AI-generated music services Udio and Suno AI were sued by a group of major record labels over copyright infringement concerns.
In March 2020, 15.ai was founded. 15.ai allowed for audio deepfakes. Artificially generated vocals can be generated with tools such as ElevenLabs, which allows for the creation of vocals from any audio. This allows for any celebrity or politician who has voice clips on the internet to be subject to audio deepfakes for both speech and singing. The voices of politicians, such as Joe Biden, have been used for fake robocalls to voters to attempt to manipulate elections.
The use of AI leads to a request and the needs of more energy. To meet this enormous demand, engineering realities show that the U.S. gas production will increase by 10 to 20% by 2030, as most of the additional electricity generation will mostly come from burning natural gas. Then, after 2030, those demands will probably be met by additional solar capacity and nuclear energy.
Electricity consumed by hardware used for AI has increased demands on power grids, which has led to prolonged use of fossil fuel power plants which would otherwise have been deactivated, since renewable energy sources often lack the continuous baseload power required for 24/7 AI model training and inference.
Microsoft, Google, and Amazon have all invested in existing or proposed nuclear power plants to meet these demands. In September 2024, Microsoft signed a deal with Constellation Energy to purchase power from a reactor at Three Mile Island which had been shut down in 2019. The reactor is set to reopen in 2028 to provide power to Microsoft's data centers. The reactor is next to the unit which caused the worst nuclear power accident in US history in 1979.
According to a report from Pew Research, opinions on AI are divided, with most Americans expressing concerns over a lack of control over AI and potential negative effect on human creativity. Nikolova & Angrisani (2025) found that people are specifically distrustful of the use AI in personal relationships, while being more accepting of its use in medicine, such as for creating new antibiotics.
Some economists have been optimistic about the potential of the current wave of AI to boost productivity and economic growth. Notably, Stanford University economist Erik Brynjolfsson, in a series of articles has argued for an "AI-powered Productivity Boom" and a "Coming Productivity Boom". At the same time, others like Northwestern University economist Robert Gordon remain more pessimistic. Brynjolfsson and Gordon have made a formal bet, registered at long bets, about the rate of productivity growth in the 2020s, to be resolved at the end of the decade.
Big Tech companies view the AI boom as both opportunity and threat; Alphabet's Google, for example, realized that ChatGPT could be an innovator's dilemma-like replacement for Google Search. The company merged DeepMind and Google Brain, a rival internal unit, to accelerate its AI research.
The market capitalization of Nvidia, whose GPUs are in high demand to train and use generative AI models, rose to over US$3.3 trillion, making it the world's largest company by market capitalization as of 19 June 2024 and became the first company to reach US$4 trillion on 9 July 2025 and subsequently US$5 trillion on 29 October later that same year.
In 2023, San Francisco's population increased for the first time in years, with the boom cited as a contributing factor.
Machine learning resources, hardware or software can be bought and licensed off-the-shelf or as cloud platform services.[99] This enables wide and publicly available uses, spreading AI skills. Over half of businesses consider AI to be a top organizational priority and to be the most crucial technological advancement in many decades.
Across industries, generative AI tools are becoming widely available through the AI boom and are increasingly used in businesses across regions. A main area of use is data analytics. Seen as an incremental change, machine learning improves industry performance. Businesses report AI to be most useful in increased process efficiency, improved decision-making and strengthening of existing services and products. Through adoption, AI has already positively influenced revenue generation in multiple business functions. Businesses have experienced revenue increases of up to 16%, mainly in manufacturing, risk management and research and development.
AI and generative AI investments have been increasing with the boom, increasing from $18 billion in 2014 to $119 billion in 2021. Most notably, the share of generative AI investments was around 30% in 2023. Further, generative AI businesses have seen considerable venture capital investments even though regulatory and economic outlooks remain in question.
Tech giants capture the bulk of the monetary gains from AI and act as major suppliers to or customers of private users and other businesses.
With the introduction of AI, there has been an exponential rise in production for businesses. It's expected that workers could use resources provided by artificial intelligence to boost their productivity. As many small businesses don't use AI, it's believed that if it's adopted by more businesses, the whole work structure could be changed, as many tasks will be automated by AI.
The U.S. Census Bureau's Business Trends and Outlook Survey measured AI use at 3–9% of businesses in March 2025, using language that asked whether firms used AI "to produce goods and services". After revising the question to cover "any business function", the Bureau's measured adoption rate increased to 18% in March 2026.
According to Bell & Korinek (2023), the economic effect of AI could worsen economic inequality, which could potentially threaten democracy in ways which are separate from threats caused by AI's use in misinformation and propaganda.
As of second quarter 2026, Nvidia is the world's largest corporation by capitalization, and semiconductor manufacturers TSMC, Broadcom, and Samsung are among the ten largest.
31-35, F